Distinguishing between central and obstructive sleep apnea

ABSTRACT

Apparatuses and methods for detecting sleep apnea and classifying the events as obstructive sleep apnea (OSA) and/or central sleep apnea (CSA) are disclosed herein. The apparatuses can include respiratory treatment devices that have an auto adjusting algorithm that is able to classify a sleep apnea as CSA or OSA so that an appropriate pressure can be applied to the patient depending on the type of sleep apnea detected. The apparatuses and methods can use characteristics of at least one breath preceding the apnea event in classifying the event.

INCORPORATION BY REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/785,291, filed Oct. 16, 2015, entitled “DISTINGUISHING BETWEENCENTRAL AND OBSTRUCTIVE SLEEP APNEA”, which is a national phase entry ofPCT Application No. PCT/M2014/060787, filed Apr. 17, 2014, entitled“DISTINGUISHING BETWEEN CENTRAL AND OBSTRUCTIVE SLEEP APNEA,” whichclaims priority to U.S. Provisional Application No. 61/813,081, filedApr. 17, 2013, entitled “APPARATUS AND TECHNIQUE FOR DISTINGUISHINGBETWEEN CENTRAL AND OBSTRUCTIVE SLEEP APNEA”. Any and all applicationsfor which a foreign or domestic priority claim is identified above or inthe Application Data Sheet as filed with the present application arehereby incorporated by reference under 37 CFR 1.57.

FIELD

The present disclosure generally relates to devices for treating sleepdisorder breathing. More particularly, the present disclosure relates todevices capable of distinguishing between obstructive sleep apnea andcentral sleep apnea.

BACKGROUND

Respiratory disorders deal with the inability of a sufferer to effect asufficient exchange of gases with the environment, leading to animbalance of gases in the sufferer. These disorders can be attributed toa number of different causes. For example, the cause of the disorder maybe (1) a pathological consequence of an obstruction of the airway, (2)insufficiency of the lungs in generating negative pressure, (3) anirregularity in the nervous function of the brain stem, or some otherdisorder. Treatment of such disorders is diverse and depends on theparticular respiratory disorder being targeted.

In the first instance, a constriction of the airway, otherwise known asan obstructive apnea or a hypopnea, collectively referred to asobstructive sleep apnea (OSA), can occur when the muscles that normallykeep the airway open in a patient relax during slumber to the extentthat the airway is constrained or completely closed off, a phenomenonoften manifesting itself in the form of snoring. When this occurs for asignificant period of time, the patient's brain typically recognizes thethreat of hypoxia and partially wakes the patient in order to open theairway so that normal breathing may resume. The patient may be unawareof these occurrences, which may occur as many as several hundred timesper session of sleep. This partial awakening may significantly reducethe quality of the patient's sleep, over time potentially leading to avariety of symptoms, including chronic fatigue, elevated heart rate,elevated blood pressure, weight gain, headaches, irritability,depression, and anxiety.

Obstructive sleep apnea is commonly treated with the application ofcontinuous positive airway pressure (CPAP) therapy. Continuous positiveairway pressure therapy involves delivering a flow of gas to a patientat a therapeutic pressure above atmospheric pressure that will reducethe frequency and/or duration of apneas and/or hypopneas. This therapyis typically delivered by using a continuous positive airway pressuredevice (CPAP device) to propel a pressurized stream of air through aconduit to a patient through an interface or mask located on the face ofthe patient.

Central sleep apnea (CSA) is a type of sleep apnea where the patientstops breathing due to lack of respiratory drive from the brain. CSA isprevalent in approximately 3 to 6% of patients with sleep disorderbreathing. However, CSA prevalence drops to about 1.5% after six weekson CPAP therapy because some patients adapt to the CPAP therapy. UnlikeOSA, there is no evidence to-date that CSA can be treated with anincrease in positive airway pressure (PAP). On the contrary, somestudies suggest that the increase in pressure provided by PAP cantrigger additional events called induced CSA. When CSA is detected, itis currently recommended that there be no pressure change responses.

SUMMARY OF THE DISCLOSURE

Some respiratory treatment devices currently classify all sleep apnea itdetects as an obstructive sleep apnea and increases the positive airwaypressure appropriately to treat OSA. However, a patient with sleepdisorder breathing can suffer from one or both of OSA or CSA. Thus, itis important to determine whether a particular apnea is caused by anobstruction or a neurological response so that an appropriate devicereaction can be determined.

The present disclosure describes a CPAP device that includes an autoadjusting algorithm that is able to classify a sleep apnea as either aCSA or OSA or combination of both so that an appropriate pressure can beapplied to the patient depending on the type of sleep apnea detected. Inan embodiment, a passive machine with a learning algorithm can be usedto derive a sleep apnea classifier model. The classifier can be derivedfrom previously recorded data samples (training data). For example, flowsignals can be collected during sleep periods of several patients. Ofparticular interest are the signals immediately before and after apneaevents. The apnea events in the training data are labeled into apneaclasses, such as, for example, OSA vs non-OSA, CSA vs non-CSA, OSA vsCSA.

In an embodiment, the determination of OSA or CSA is made by looking ata number of breaths that occur directly before an apnea event. In oneembodiment, the 8 breaths that occurred prior to the apnea are analyzed.The pre-apnea breaths' morphology is analyzed to determine thecharacteristics of the breaths. The characteristics are compared topre-determined data of patients experiencing known OSA and CSA events.The comparison information provides an indication of whether the patientis experiencing an OSA event or a CSA event. In an embodiment, themorphology characteristics of the pre-apnea breaths are combined todetermine a final morphology score. In an embodiment, this score is aprobability distribution. The score or probability distribution is thencompared with threshold information determined from empirical data todetermine if the apnea is an OSA or CSA apnea.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features, aspects and advantages of the presentdisclosure will now be described with reference to the drawings of apreferred embodiment, which embodiment is intended to illustrate and notto limit the invention, and in which figures:

FIG. 1 is a high level flow chart of an OSA/CSA classification system.

FIG. 1A is a flow diagram of an OSA/CSA determination embodiment of thedisclosure.

FIG. 2 is a flow diagram illustrating additional details of theembodiment of FIG. 1A.

FIG. 3 is a flow chart of an apnea classification process.

FIG. 4 is a flow chart of a breath feature extraction process.

FIG. 5 is a graphical illustration of deriving a standard breath.

FIG. 6 is a diagram illustrating breath windows of pre-apnea breathsused for apnea feature extraction.

FIG. 7 is a graphical illustration of breaths in a pre-apnea breathwindow used for classification of an apnea.

FIG. 8 is a graphical illustration of a breath during an apnea.

FIG. 9 is a graphical illustration of a breath-window configuration whenbreaths during an apnea are detected.

FIG. 10 is a graphical illustration of breath features.

FIG. 11 is a diagram illustrating the formation and population of anapnea feature array.

FIG. 12 is an embodiment of an apnea classification flow chart.

FIG. 13 illustrates an embodiment of a CPAP system.

FIG. 14 illustrates an embodiment of hardware components of a CPAPsystem.

DETAILED DESCRIPTION

Certain features, aspects and advantages of the present disclosurerelate to a method for automated adjustment of respiratory treatmentdevices, such as CPAP devices, and automated classification of sleepapnea type. In some configurations, the method can be implemented usinga sleep apnea classification algorithm and an auto adjustment algorithmthat are implemented and integrated as part of an overall CPAP controlstructure. An embodiment of a CPAP system and its corresponding majorhardware components are described with respect to FIGS. 13 and 14.

As described herein, the sleep apnea classification algorithm canclassify an apnea detected by the apnea detector as OSA or CSA byanalyzing the characteristics of the breaths surrounding the apnea.

FIG. 1 shows a flow chart of an embodiment of a process for determiningwhether an apnea should be classified as an OSA or CSA event. Theprocess starts at step 101 where breath flow signals are acquired andanalyzed by a device providing positive air flow pressure to a patient.The system periodically determines from the breath flow data at step 103if an apnea has been detected. If an apnea has not been detected, thesystem continues to monitor the breath flow uninterrupted. If an apneais detected at step 103, then the system moves to stop 105 where theapnea is classified as an OSA event or a CSA event. In an optionalembodiment, the event information is reported at step 109. Alsooptionally, once the apnea is classified, the system can determine anappropriate pressure response at step 107. The system then returns tostep 101 and continues to monitor the patient. At step 107, the pressureresponse can be an increase in pressure, a decrease in pressure, or noresponsive pressure changes. In an embodiment, if the apnea is an OSAevent, the pressure is increased. In an embodiment, if the apnea is aCSA event, the pressure is maintained or decreased. In an embodiment, ifthe apnea is a CSA event, the pressure is increased if a currentpressure is below a CSA pressure limit. In an embodiment, the CSApressure limit is about 10-20 cmH₂O. In other embodiments, the CSApressure limit is about 12-15 cmH₂O.

FIG. 1A illustrates another embodiment of an apnea detection andclassification system. In the first step, the flow data 110 is acquired.In the second step of the process, the acquired flow data is analyzed bybreath detector 120 and apnea detector 130. The breath detector 120determines individual and group breath information. The apnea detectordetermines if an apnea is believed to have occurred. The output of eachdetector can be stored into respective storage tables. When a breath isdetected, information, such as any of a variety of breath features 124,can be determined and stored in a breath table 122. When an apnea isdetected, the apnea information can be stored in an apnea table 132.

During the analysis phase, the apnea table 132 is regularly checked, forexample, after every valid breath, for a detected apnea that is ready tobe classified. An apnea is considered ready to be classified if apre-defined number of expected breaths (such as, for example, one breathor a group of breaths in certain implementations) or period of time haspassed since the apnea was detected. The apnea then can be classified bythe apnea classifier 140 using information from the breath table 122 andapnea table 132. The apnea can be classified into an OSA class or a CSAclass or another appropriate class.

Finally, the optional pressure adjustment module 150 can use the classof the apnea to determine an appropriate pressure response for thedetected apnea. In an embodiment, an apnea event can be deleted, ordeclared finished, from the apnea table 132 once the number of postapnea breaths for the apnea is greater than or equal to the maximumnumber of post apnea breaths that are stored (such as, for example, 1 ormore breaths in certain implementations). This allows the system to moveonto a new apnea event where the process is repeated

FIG. 2 illustrates greater details of the apnea classifier 140. Theapnea classifier 140 receives as inputs the parameters of an apnea inthe apnea table 132 and the breath features of breath table 122 relevantto the apnea. The apnea classifier 140 uses the flow index analysis anda length of an apnea from the apnea table 132 to determine which breathsin the breath table 122 to use for apnea classification.

Once the corresponding breaths of an apnea are determined, the apneaclassification module 210 of apnea classifier 140 can requestinformation from the breath table 122. The breath information obtainedfrom the breath table 122 is used in conjunction with the apneainformation obtained from the apnea table 132 to classify the apnea. Theapnea classifier outputs the predicted type of the apnea. The apnea typeis fed back to the apnea table 132 where it can be stored in an apneatype field entry of the respective apnea in the apnea table 132.

With reference to FIG. 3, a flow chart for an apnea classificationprocess is illustrated. The flow chart can be implemented as part of abreath analysis process of a CPAP device control software. Thisillustrated process is repeated throughout the sleep session. Thisprocess can be implemented as part of the processes described above withrespect to FIGS. 1, 1A and 2.

FIG. 3 begins at step 310 where the system receives flow data andextracts and stores breath features. When an apnea is detected, theapnea classification can be carried out once the apnea is populated inthe apnea table and enough valid breaths have passed since the detectionof the apnea to indicate the conclusion or ending of the apnea asdetermined at step 320. In some embodiments of the apnea classificationmodel, only pre-apnea breaths are analyzed to classify the apnea. As aresult, in these embodiments, only a single or small number ofpost-apnea valid breaths may be needed before apnea classification.Alternatively, the classification can be performed once an apnea hasbeen detected via an apnea detection algorithm and there is no need towait until the first breath after the apnea to complete theclassification. In other embodiments, additional post-apnea breaths aredetected before the apnea is classified.

Once a sufficient number of post apnea breaths have been detected, thenext step in the illustrated apnea classification process occurs at step330 which involves creating a standard breath and extracting the breathfeatures of the standard breath. The standard breath is determined inorder to effectively normalize the detected breaths to make the systemgenerally invariant to the scale of the flow data. The standard breathcan be determined from either pre-apnea breath information, post-apneabreath information or both pre and post apnea breath information. In anembodiment, a group of pre-apnea breaths is used to determine thestandard breath. In an embodiment 1 to 12 breaths are used. In anembodiment, 8 breaths are used. The use of only pre-apnea breaths in thedetermination of the apnea type allows the apnea classification to occursooner because only one or a few post apnea breaths are necessary beforethe classification can occur. In such embodiments, because the apneaclassification can occur more quickly than waiting for and analyzingpost apnea breaths, pressure decisions can be made sooner, leading to amore responsive CPAP system.

In step 340, the apnea is classified by calculating the apnea featuresand then using those apnea features with an apnea classifier model todetermine the apnea type. The method for the apnea feature calculationand apnea classification can be implemented in a module within thecontrol software and will be further discussed below.

FIG. 4 illustrates greater details of step 310 of FIG. 3. The process ofstep 310 begins at step 311 by first analyzing the flow data. Theprocess then determines if a valid breath has been detected at step 313.If a valid breath is detected, then the morphological features of thebreath are extracted at step 315. The extracted breath features areadded to the breath table 122 at step 317. Optionally, at step 319 abreath count is incremented. The breath count can be the number ofbreaths recorded since the end of a previous apnea, the number ofbreaths recorded in a session or any other meaningful breath count. Themorphological features analyzed are explained in greater detail hereinbelow.

Standard Breath Determination

Standard breaths can be used for normalisation of the breath-features ofthe detected breaths because the scale of the flow data cansignificantly affect the classifier calculation. Scale differences occurdue to a variety of uncontrollable factors including, for example,sensor sensitivity, patient physiology, pressure changes within thesystem, etc. The normalisation of breath-features with a local standardbreath makes the apnea classification method generally invariant to thescale of the flow data. Breath features of each detected breath can benormalised by dividing it with a corresponding breath-feature of thestandard breath.

A standard breath is a sinusoidal representation of average breathfeatures derived from a local breath-window at a certain point in a timeseries. For apnea classification, the breath-window with a number ofpre-apnea breaths can be used to derive average breath parameters toform a standard breath. In an embodiment, this can include a windowcontaining the maximum number of pre-apnea available breaths. In anembodiment, the window contains a set number of breaths, for example, 8breaths. However, under certain circumstances, such as when an apnea isdetected just after an end of a CPAP mask leak, there might be lessbreaths in the breath table than required. In these cases, where theapnea classification function is not ready, the standard breath can bederived with the available pre-apnea breaths detected after the leak hasstopped. In addition, breaths detected during apnea also can be excludedfor derivation of the standard breath used during the apneaclassification method.

As shown in FIG. 5, the standard breath is derived by fitting tworespective sinusoids to average inspiration and expiration measures ofbreaths in a pre-defined breath-window. The two sinusoids can be derivedfrom the mean of maximum flow, the mean duration of inspiration, themean minimum flow, and/or the mean total breath duration over thebreath-window. Of course, other morphological features can be used todetermine the standard breath, for example, any of the features listedin Table 1 below. Once the standard breath is derived, the breathfeatures of the standard breath can be calculated in the same way orsimilar to every other detected breath.

Apnea Classification

The apnea classification module 210 contains the functions that are usedto classify an apnea. In an embodiment, the process of apneaclassification can involve extraction of the pre-apnea breath features,using the pre-apnea breath features with a model to calculate aprobability distribution and finally deciding the class of an apnea bycomparing the probability distribution to a predetermined threshold. Thebreath-window configuration and the parameters of the model can bedefined in this module.

Any number of different models can be used with the present system. Forexample, a logistics model or simple logistics model can be used. Inother embodiments, a neural network, multiple perceptron model orsupport vector machine model can be used. Of course, it is to beunderstood that other models or combinations of models can be used aswell.

Apnea Feature Extraction

In some embodiments, the first step in classification of an apnea is toextract apnea features. Extraction of apnea features involves capturingstatic and/or temporal characteristics of a flow signal (including, forexample, breath data) preceding and following a sleep apnea. The apneafeatures are derived by calculating statistical metrics of breathfeatures within predefined breath-windows.

Apnea Windows

The process of apnea breath feature extraction can involve acquiring thebreath-features for pre-defined breath-windows and then calculating thebreath-feature statistics for each breath-window. A breath-window can bea pre-defined number of breaths either before and/or after a sleepapnea. The statistics of the breath-features within each breath-windowcapture characteristics of the flow signal during that period. The useof multiple breath-windows allows the capture of flow signalcharacteristics over different time periods. The apnea classificationmodel can learn the difference in flow characteristics surrounding eachapnea. FIG. 6 illustrates a breath-window configuration that consists of3 pre-apnea breath-windows, W1, W2, and W3 with 2, 4, and 6 breathsrespectively. Other breath window configurations could be used as well.For example, in an embodiment, 8 pre-apnea breaths are used with fourdifferent over lapping or independent windows. In an embodiment, thefirst window has two breaths, the second window has 4 breaths, the thirdwindow has six breaths and the fourth window has all eight breaths.Other configurations, numbers of windows or numbers of pre or post apneabreaths can also be used.

Although the configuration of FIG. 6 uses only the pre-apneabreath-windows, it is possible to use both pre-apnea and post-apneabreath-windows. Using both pre-apnea and post-apnea windows allows thecapture of flow characteristics both before and after an apnea. However,one disadvantage of using a post-apnea window is that the pressureresponse to a detected apnea will be delayed because the classificationof an apnea can only be performed once all the required post-apneabreaths have passed. Because of this delay in classification performancewith the post-apnea window, some embodiments of the present disclosureonly use pre-apnea windows. In some configurations, the software modulecan be implemented to be able to use pre and post apnea windows as wellas only one of the pre and post apnea windows.

Determination of Start of Pre-Apnea Breath

For each breath-window, there is a determination of which breaths to usewithin the breath table. In some configurations, there are two criteriaunder which a breath may be excluded for apnea classification. Forexample, the breath may be avoided if: a breath turns into an apnea(apnea-breath); or a breath was detected during an apnea.

The apnea-breath is determined by comparing the start and end flow indexof an apnea and breaths in the breath table at the time the apnea wasdetected. By comparing the flow index of the end of the breath to theflow index of the start of the apnea in order of the newest breath tothe oldest breath in the breath table, the newest breath after the apneabreath can be determined. In other words, the index (S) of the newestbreath in the breath table, whose end of the breath flow index is lessthan the start of the apnea flow index, can be chosen as the start ofthe pre-apnea breath. Examples of the pre-apnea breaths used for apneaclassification are shown in FIG. 7.

The newest pre-apnea breath defined by the breath table index S andbreaths preceding it in the breath table are used for apneaclassification.

Exclusion of Breaths During Apnea

In addition to the exclusion of the apnea-breaths, if a breath within anapnea breath-window is marked as a breath during an apnea (BDA), it isalso not used for apnea classification. The BDA breaths can beconsidered to be a false detection of a breath because the presence of abreath during a sleep apnea generally is contradictory to the definitionof a sleep apnea. In addition to the above reasons, BDA also may beavoided because they can produce extreme breath feature values (such asextremely long breath lengths), which can have undesirable effects onthe apnea features because it is calculated based on the statistics ofthe breath-features in the breath-window.

A BDA is most likely to be present within a breath-window if two or moreapneas occur one after another with only few breaths between them. Anexample of a BDA breath when two apneas occur within 4 breaths of eachother is illustrated in FIG. 8. In this example, the second BDA breathA1 is the apnea-breath of a preceding apnea to apnea A2.

For a breath-window, if there is less than the required non-BDA breathsafter the exclusion of the BDA breaths, the number of breaths in thebreath-window can be increased either until there are enough none-BDAbreaths or the number of breaths has reached the breaths size of thelargest breath-window. For example, in FIG. 9, because the firstbreath-window (w₁) requires 2 non-BDA breaths, the window size isincreased to 4 because the 2 latest breaths are BDA breaths. Similarly,because the second breath-window (w₂) requires 4 non-BDA breaths, itssize is increased to 6 breaths. In this example, the third breath-window(w₃) is the largest breath-window, hence the number of breaths could notbe increased, and the apnea feature for w₃ is calculated based on the 4non-BDA breaths. As a result, the apnea features for breath-windows w₂and w₃ end up being the same.

In some configurations, the number of windows that can be increased toacquire all required non-BDA breath can be fixed to the size of thelargest breath-window to avoid using breaths that are too far in thepast, which has little to no relevance to the apnea being classified. Ifthere are less than 2 non-BDA breaths within any breath-window, theapnea feature calculation will not operate because there are not enoughbreaths to calculate feature statistics for the window.

Apnea-Feature

The next step in the illustrated apnea feature calculation is acquiringthe values of selected breath features for non-BDA breaths in thebreath-window. An example of the breath features is shown in FIG. 10. Insome configurations, there are 27 breath features calculated for eachvalid breath and populated into the breath table. In an embodiment, all27 features are used. In an embodiment, a subset of features is used. Ofcourse, it should be understood by those of skill in the art thatvarious combinations and sub-combinations of the below listed featuresor other available features can be used. The features listed areprovided by way of illustration and not limitation. In some embodiments,less than all available or used features can be obtained for modeltraining purposes.

Table 1 provides a list of 27 possible breath-features

TABLE 1 Breath features used for development of apnea classificationmodel B Breath Features 1 Flow Index 2 Max Inspiration Amplitude 3 Timeof Max Inspiration Amplitude 4 Breath duration 5 Inspiration duration 6Inspiration duration/Breath duration 7 Max. Expiration Amplitude 8 Timeof Max Expiration Amplitude 9 Maximum Expiration Time - Time of MaxInspiration 10 Maximum Inspiration Amplitude - Maximum ExpirationAmplitude 11 Max. Inspiration Acceleration{circumflex over ( )} 12 Timeto Max. Inspiration Acceleration{circumflex over ( )} 13 InspirationVolume 14 Expiration volume 15 Volume ratio 16 Amplitude InspirationCenter of Mass (CM) 17 Time inspiration CM 18 Amplitude expiration CM 19Time Expiration CM 20 Time Expiration CM - Time Inspiration CM 21Amplitude expiration CM - Amplitude Inspiration CM 22 Maximum Flow Rate*23 Time of Maximum Flow Rate* 24 Time of Maximum negativeacceleration{circumflex over ( )} 25 Time of the maximum positiveacceleration{circumflex over ( )} 26 Maximum negativeacceleration{circumflex over ( )} 27 Maximum positiveacceleration{circumflex over ( )} *derived from 1^(st) derivative of theflow signal in an embodiment {circumflex over ( )}derived from 2^(nd)derivative of the flow signal in an embodimentBreath Feature Normalization and Apnea Feature Statistics

The values for the breath features selected for apnea classification areacquired from the breath table for breaths within breath-windows. Then,the breath-features for the BDA breaths are removed. Each breath-featureis then divided by the corresponding breath feature of the standardbreath to derive normalized breath-features of non-BDA breaths. The meanand standard-deviations of the normalized breath-features are calculatedto derive apnea features.

Apnea Feature Vector (A_(F))

The apnea-features for each window are stored in memory to form an arrayof apnea-feature vectors (A_(F)) as shown in FIG. 11. The size of A_(F),(N_(AF)) is a product of the number of breath-features used (N_(BF)),the number of breath-windows (N_(w)), and the number of statisticalmetrics (N_(s)) used to derive the apnea-features:N _(AF) =N _(w) *N _(BF) *N _(s)Model Features

Apnea-features derived from empirically obtained data from a large crosssection of patients are used for training the apnea classification modelduring the design phase. The apnea classification model can be trainedbased on a machine learning algorithm which, in addition to deriving theclassification model, also selects a subset of optimal apnea-features(a) from A_(F). The optimal apnea-features (a) can be used in the finalapnea classification model and can be the main output of the apneafeature extraction function.

Apnea Classification

To classify a sleep apnea, the apnea-features subset (a) is used in theapnea classification model to derive a probability distribution (PD),which in-turn is compared with a predetermined threshold (TPD) todetermine the class of the sleep apnea. The flow chart in FIG. 12outlines an embodiment of an apnea classification process that can beimplemented. The process starts at step 1201 where the process sensesthat an apnea has occurred. At step 1203, the pre-apnea (and/orpost-apnea) breath data is analyzed. At step 1205, the analyzed breathdata is normalized. The normalized breath data is then applied to amodel that has been trained with empirically determined data at step1207. A probability distribution is determined as explained hereinbelow. The probability distribution is then compared to a threshold atstep 1209 in order to determine the apnea classification.

Simple Logistics Apnea Classification

In an embodiment, a simple logistics model is used. The simple logisticsmodel is used as a classifier to provide an output probabilitydistribution for apnea classes. The probability distribution(PD_(a,OSA)) of an OSA class given the apnea-features a can be definedby following equation:

$\begin{matrix}{{PD}_{a,{OSA}} = e^{W_{0,{OSA}} + {\sum\limits_{j = 1}^{Na}\;{W_{j,{OSA}} \times a_{j}}}}} & {{Equation}\mspace{14mu} 1}\end{matrix}$where W_(0,OSA) is the model bias, W_(j,OSa) are the model weights forOSA class, and a is the corresponding apnea-features

Similarly, the equation for CSA probability distribution (PD_(a,CSA))is:

$\begin{matrix}{{PD}_{a,{OSA}} = e^{- {({W_{0,{OSA}} + {\sum\limits_{j = 1}^{Na}\;{W_{j,{OSA}} \times a_{j}}}})}}} & {{Equation}\mspace{14mu} 2}\end{matrix}$

The output of each class is then normalized to value from 0 to 1 usingthe equation below:

$\begin{matrix}{{PD} = \frac{{PD}_{a,{OSA}}}{{PD}_{a,{OSA}} + {PD}_{a,{CSA}}}} & {{Equation}\mspace{14mu} 3}\end{matrix}$

The PD for a sleep apnea event is then compared with the TPD as shown inEquation 4 to determine the apnea class.

-   -   If PD<0 OR PD>1        Apnea Class=None    -   Else If PD>=TPD        Apnea Class=Obstructive    -   Else        Apnea Class=Central  Equation 4        Determination of Probability Distribution Threshold (T_(PD))

The T_(PD) can be determined by analyzing the PD for the trainingdataset calculated by the apnea classification model. T_(PD) can bechosen so that the specificity and sensitivity of the training datasetis as close to each other as possible.

Apnea Classification During Flow Leak

In some configurations, the apnea classification is not performed undera high flow leak condition because apneas may not be detected duringleak in certain CPAP device control systems. However, there can be amoment in operation just after the leak has stopped where an apneaoccurs with less than the number of breaths required to classify theapnea. In such a situation, the apnea can be classified with breathsthat are available at the time. The system can keep track of how manybreaths have passed since the leak stopped. If enough breaths haveoccurred to classify an apnea after the leak has stopped, then the apneaclassification system described herein can be used.

Overview of CPAP System

FIG. 13 illustrates an embodiment of a CPAP system 1300 according to thepresent disclosure. The system includes a CPAP device 1302, a tube 1304and a mask 1306. The mask 1306, in use, is positioned over one or bothof a nose and face of the patient to supply a positive air pressurethereto. In an embodiment, the CPAP system 1300 also includes a sensorpositioned in one or both of the mask 1306 and tube 1304 in order tomonitor air flow or pressure there through. In an embodiment, the sensoris a flow sensor placed in the system before the blower or anywherewhere flow or pressure information can be obtained. In an embodiment,the sensor is a pressure sensor placed in the system after the blower,but before the humidifier. As will be understood by those of skill inthe art, the sensor can be any of a number of sensors located at variouslocations in the system that are capable of detecting operatingcharacteristics of the system and flow or pressure states of thepatient. The CPAP device can be an auto-titrating device, a bi-leveldevice, a single pressure device, or any combination of the aforementioned CPAP pressure devices.

FIG. 14 illustrates a schematic depiction of CPAP device 1302. CPAPdevice 1302 includes a controller 1401, an air supply 1403, a memorydevice 1405, and optionally a display 1407 and user inputs 1409. Thecontroller 1401 controls the operation of the CPAP device 1302. Thecontroller 1401 can include, for example, analog or digital processorsor other electronic control devices as would be understood by a personof skill in the art from the present disclosure. The controller 1401controls the operation of the air supply 1403. The controller alsoreceives and analyzes sensor signals from sensor(s) 1408. The controller1401 communicates with the memory device 1405 to store informationincluding operating information, sensor information, and otherinformation as would be understood by a person of skill in the art andas disclosed herein. The controller can also optionally receive commandsfrom a user input 1409 as well as communicate output information to thedisplay 1407.

CONCLUSION

Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey that certainembodiments include, while other embodiments do not include, certainfeatures, elements and/or steps. Thus, such conditional language is notgenerally intended to imply that features, elements and/or steps are inany way required for one or more embodiments or that one or moreembodiments necessarily include logic for deciding, with or without userinput or prompting, whether these features, elements and/or steps areincluded or are to be performed in any particular embodiment.

Any process descriptions, elements, or blocks in the flow diagramsdescribed herein and/or depicted in the attached figures should beunderstood as potentially representing modules, segments, or portions ofcode which include one or more executable instructions for implementingspecific logical functions or steps in the process. Alternateimplementations are included within the scope of the embodimentsdescribed herein in which elements or functions may be deleted, executedout of order from that shown or discussed, including substantiallyconcurrently or in reverse order, depending on the functionalityinvolved, as would be understood by those skilled in the art. It willfurther be appreciated that the data and/or components described abovemay be stored on a computer-readable medium and loaded into memory ofthe computing device using a drive mechanism associated with a computerreadable storing the computer executable components such as a CD-ROM,DVD-ROM, memory stick, or network interface. Further, the componentand/or data can be included in a single device or distributed in anymanner. Accordingly, general purpose computing devices may be configuredto implement the processes, algorithms and methodology of the presentdisclosure with the processing and/or execution of the various dataand/or components described above.

Although the present invention has been described in terms of certainembodiments, other embodiments apparent to those of ordinary skill inthe art also are within the scope of this invention. Thus, variouschanges and modifications may be made without departing from the spiritand scope of the invention. For instance, various components may berepositioned as desired. Moreover, not all of the features, aspects andadvantages are necessarily required to practice the present invention.

What is claimed is:
 1. A positive airway pressure apparatus, theapparatus comprising: a blower; a sensor for measuring a respiratoryairflow rate or pressure; and a controller configured to: detect apresence of an apnea event based on an output of the sensor, measure oneor more characteristics of at least one breath preceding the apneaevent, and classify the apnea event as central sleep apnea orobstructive sleep apnea based on use of the one or more characteristicsin the at least one breath preceding the apnea event in a model, whereinthe model excludes use of breaths during the apnea event.
 2. Theapparatus of claim 1, wherein the controller is configured to measurethe one or more characteristics of only breath(s) preceding the apneaevent.
 3. The apparatus of claim 1, wherein the controller is configuredto classify the apnea by comparing a threshold value to a function ofthe one or more characteristics.
 4. The apparatus of claim 1, whereinthe at least one breath preceding the apnea event is in a pre-apneabreath window.
 5. The apparatus of claim 1, wherein the one or morecharacteristics comprises one or more of flow index, max inspirationamplitude, breath duration, inspiration duration, expiration duration,maximum expiration amplitude, time of maximum expiration amplitude,maximum expiration time to time of maximum inspiration, maximuminspiration amplitude to maximum expiration amplitude, maximuminspiration acceleration, time to maximum inspiration acceleration,inspiration volume, expiration volume, volume ratio, amplitudeinspiration center of mass (CM), time inspiration CM, amplitudeexpiration CM, time expiration CM, time expiration CM to timeinspiration CM, amplitude expiration CM to amplitude inspiration CM,maximum flow rate, time of maximum flow rate, time of maximum negativeacceleration, time of the maximum positive acceleration, maximumnegative acceleration, maximum positive acceleration, or anycombinations thereof.
 6. The apparatus of claim 1, wherein thecontroller is configured to normalize the one or more characteristicsrelative to a standard.
 7. The apparatus of claim 6, wherein thestandard is derived from the at least one breath preceding the apneaevent.
 8. The apparatus of claim 6, wherein the standard is arepresentative average breath calculated from breaths preceding theapnea event.
 9. The apparatus of claim 1, wherein the controller isconfigured to derive a probability distribution with the use of the oneor more characteristics in the model, and classify the apnea event ascentral sleep apnea or obstructive sleep apnea based on a comparison ofthe probability distribution to a predetermined threshold.
 10. Theapparatus of claim 1, wherein the controller is configured to increasethe pressure if the apnea event is classified as obstructive sleepapnea.
 11. The apparatus of claim 1, wherein the controller isconfigured to maintain or decrease the pressure if the apnea event isclassified as central sleep apnea.
 12. The apparatus of claim 1, whereinthe model comprises a trained classifier.
 13. The apparatus of claim 1,wherein the controller is configured to classify the apnea event ascentral sleep apnea or obstructive sleep apnea based on use of the oneor more characteristics of only breaths preceding the apnea event in amodel.
 14. A method of providing respiratory therapy to a patient usinga positive airway pressure apparatus, the method comprising: detecting apresence of an apnea event experienced by the patient based on an outputof a sensor of the apparatus for measuring a respiratory airflow rate orpressure delivered to the patient by the positive airway pressureapparatus, wherein the positive airway pressure apparatus uses a blowerto generate positive airway pressure; measuring one or morecharacteristics of at least one breath of the patient preceding theapnea event; and classifying the apnea event as central sleep apnea orobstructive sleep apnea based on use of the one or more characteristicsin the at least one breath preceding the apnea event in a model, whereinthe model excludes use of breaths during the apnea event.
 15. The methodof claim 14, further comprising measuring the one or morecharacteristics of only breath(s) preceding the apnea event.
 16. Themethod of claim 14, further comprising classifying the apnea bycomparing a threshold value to a function of the one or morecharacteristics.
 17. The method of claim 14, wherein the at least onebreath preceding the apnea event is in a pre-apnea breath window. 18.The method of claim 14, wherein the one or more characteristicscomprises one or more of flow index, max inspiration amplitude, breathduration, inspiration duration, expiration duration, maximum expirationamplitude, time of maximum expiration amplitude, maximum expiration timeto time of maximum inspiration, maximum inspiration amplitude to maximumexpiration amplitude, maximum inspiration acceleration, time to maximuminspiration acceleration, inspiration volume, expiration volume, volumeratio, amplitude inspiration center of mass (CM), time inspiration CM,amplitude expiration CM, time expiration CM, time expiration CM to timeinspiration CM, amplitude expiration CM to amplitude inspiration CM,maximum flow rate, time of maximum flow rate, time of maximum negativeacceleration, time of the maximum positive acceleration, maximumnegative acceleration, maximum positive acceleration, or anycombinations thereof.
 19. The method of claim 14, further comprisingnormalizing the one or more characteristics relative to a standard. 20.The method of claim 19, wherein the standard is derived from the atleast one breath preceding the apnea event.
 21. The method of claim 19,wherein the standard is a representative average breath calculated frombreaths preceding the apnea event.
 22. The method of claim 14,comprising deriving a probability distribution with the use of the oneor more characteristics in the model, and classifying the apnea event ascentral sleep apnea or obstructive sleep apnea based on a comparison ofthe probability distribution to a predetermined threshold.
 23. Themethod of claim 14, further comprising increasing the pressure if theapnea event is classified as obstructive sleep apnea.
 24. The method ofclaim 14, further comprising maintaining or decreasing the pressure ifthe apnea event is classified as central sleep apnea.
 25. The method ofclaim 14, wherein the model comprises a trained classifier.
 26. Themethod of claim 14, wherein classifying the apnea event as central sleepapnea or obstructive sleep apnea based on use of the one or morecharacteristics preceding the apnea event in a model comprises using theone or more characteristics of only breaths preceding the apnea event.